ArticleScientific reports2022
A data-driven ultrasound approach discriminates pathological high grade prostate cancer.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Artificial intelligence-based prostate cancer risk stratification via large-scale ultrasound images paired with histopathology.Quantitative imaging in medicine and surgery · 2026Article
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Artificial intelligence for prostate cancer detection and risk stratification using transrectal ultrasound: a narrative review.Gland surgery · 2026Review
- Clinically informed intermediate reasoning enables generalizable prostate cancer prognostication through machine learning in limited settings.NPJ digital medicine · 2025Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- An overview of utilizing artificial intelligence in localized prostate cancer imaging.Expert review of medical devices · 2025Review
- A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model.Frontiers in oncology · 2025Article
- Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction.Current oncology (Toronto, Ont.) · 2024Article
- Novel Histopathological Biomarkers in Prostate Cancer: Implications and Perspectives.Biomedicines · 2023Review
- Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.Current urology reports · 2023Review
- Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors.Life (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 (p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.